Self Supervised Learning Theory And Mathematical Foundations

via “theoretical foundation for supervised learning with neural networks”

* 🏆 1992: [A training algorithm for optimal margin classifiers (SVM)](https://dl.acm.org/doi/10.1145/130385.130401)

Unique: Connects universal approximation theory directly to the supervised learning setting by proving that networks can learn any continuous mapping from finite input-output examples, providing theoretical justification for the empirical success of neural networks in regression and classification tasks